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... 11:1
AI Engineer - Super AI Agent In God Mode!

Kingy AI

1+ months ago 13K views

AI engineer functionality in Chat LLM Teams allows users to create custom AI agents quickly. By leveraging state-of-the-art large language models, users can design AI assistants that specialize in specific domains such as skincare or quantum computing, ensuring robust responses while limiting out-of-scope information. The tool ensures transparency in the agent’s planning and execution process, providing step-by-step insight into the actions being taken. Users can upload their own data for tailored responses, enhancing AI relevance and accuracy while reducing common mistakes like hallucinations.
02:11 - 03:05

Creating a skincare-focused AI agent named Dermabot with defined restrictions.

Abacus.AI Natural Language Processing (NLP) AI Trends
... 5:39
New! A Free GUI That Makes OpenAI Agents 10x Better!

J. Gravelle

1+ months ago 1K views

OpenAI has released a new SDK for their agents, allowing users to easily create both single-purpose and cooperative agents without extensive coding. This interface, which runs in a local browser, offers features like access to built-in and custom-made tools. Users can create agents, set specific tasks like web searching for weather information, and configure various options such as guarding settings to secure agent behavior. The interface is designed for ease of interaction and even includes testing screens to validate agent functions.
03:41 - 04:01

Agents can easily be tested and refined through an interactive interface.

OpenAI Chatbots Tech & Hardware
... 27:13
Build an AI Agent That Can Use Any Tool (MCP in n8n Tutorial)

Bart Slodyczka

1+ months ago 3K views

Building an mCP agent involves integrating new tools like Airbnb and Brave within an AI framework, where instructions for interactions are retrieved and executed based on user requests. The setup includes configuring community nodes, utilizing APIs, and deploying various functionalities. Users can access and command tools for tasks such as retrieving accommodations or searching for café options, showcasing a versatile application of AI technology in practical scenarios. This implementation illustrates how different AI tools work collaboratively to automate tasks effectively. Comprehensive setup guides and troubleshooting insights are also provided throughout the discussion.
01:31 - 02:02

Searching for cafes with Brave tool, demonstrating tool use with AI.

n8n Machine Learning Education
... 4:49
From Zero to Autonomous: Essential Secrets of Enterprise Agentic AI in 5 Minutes | AI Insights

SiliconANGLE theCUBE

1+ months ago 3K views

Architecting agentic AI systems requires a focus on intelligence and autonomy, emphasizing the need for robust foundational architecture that integrates perception, reasoning, and action. Multi-agent collaboration is crucial to enhance efficiency and resource utilization, supported by effective communication and dynamic task allocation. Continuous performance optimization ensures that AI systems adapt to changing business environments, maintaining relevance in operations. Without these elements, AI deployments risk becoming misaligned with organizational goals, diminishing both efficiency and return on investment.
03:06 - 03:09

Continuous optimization is necessary to keep AI systems aligned with business needs.

Autonomous Systems AI Trends
... 37:21
Connect N8N AI Agents to EVERYTHING using MCP?

Simon Scrapes | AI Agents & Automation

1+ months ago 6K views

Connectivity and integration of AI agents with tools through the Model Context Protocol (mCP) revolutionizes automation in businesses. This unified standard simplifies interactions with various data sources, enabling seamless web scraping, real-time updates, and executing commands in a user-friendly manner. The potential of mCP is evidenced by its ability to reduce setup complexity and enhance efficiency by allowing AI systems to interact with multiple applications without deep API knowledge. Step-by-step guidance is provided to demonstrate mCP's setup and capabilities, highlighting examples such as scraping web pages and utilizing Google Maps for user assistance.
01:09 - 01:12

Overview of mCP's role in simplifying AI complex workflows and integrations.

AI cloud services AI Startups
... 25:29
Is MCP the Future of N8N AI Agents? (Fully Tested!)

The AI Automators

1+ months ago 4K views

mCP, the Model Context Protocol, represents a standardized framework for AI agents to seamlessly discover and utilize available tools and data sources without manual setup. This new architecture offers a major advantage over traditional approaches by allowing agents to dynamically access and adapt to new tools, enhancing their capabilities. The implementation involves setting up mCP clients and servers, which can communicate with various services, improving the efficiency and functionality of AI applications. The potential of mCP lies in its ability to simplify integrations and facilitate more autonomous AI operations across different platforms.
04:28 - 04:44

Discussion of mCP as a USB-like connection for AI models enhancing integration.

Machine Learning AI Startups
... 16:24
New OpenAI Agent SDK and More: What Developers Need to Know!

Prompt Engineering

1+ months ago 3K views

OpenAI has introduced several new tools for developers, enhancing the capabilities of its API. Key features include a response API that supports tool interaction, a new agent SDK for building agents, and browsing capabilities for real-time data access. The company is evolving from a foundation model provider to a more comprehensive product-oriented approach. New offerings like file search and computer use tools are set to empower developers to create innovative applications. The SDK is built on open-source projects, supporting interoperability with other platforms, while pricing details for these tools have also been provided.
03:14 - 03:40

The new agents SDK allows orchestration of single and multi-agent workflows.

OpenAI AI cloud services Tech & Hardware
... 16:24
New OpenAI Agent SDK and More: What Developers Need to Know!

Prompt Engineering

1+ months ago 3K views

OpenAI has introduced several new tools for developers, enhancing the capabilities of its API. Key features include a response API that supports tool interaction, a new agent SDK for building agents, and browsing capabilities for real-time data access. The company is evolving from a foundation model provider to a more comprehensive product-oriented approach. New offerings like file search and computer use tools are set to empower developers to create innovative applications. The SDK is built on open-source projects, supporting interoperability with other platforms, while pricing details for these tools have also been provided.
03:14 - 03:40

The new agents SDK allows orchestration of single and multi-agent workflows.

OpenAI AI cloud services Tech & Hardware